Abstract

Today, we observe an expansion of the amount of available video data. Efficient use of this data mass requires to be able to extract information from it. In this thesis, we propose to use data mining methods and apply them on video-objects of interest, in order to bridge the semantic gap by involving the user in the process. The extraction of such video-objects from pixels implies the handling of large data volume. This leads to expensive computing (in terms of computation time and memory) which is not compatible with an interactive user involvement. Thus, we propose to apply the interactive segmentation process on a data reduction, the quasi-flat zones. Quasi-flat zones are only defined for still images, so we propose an extension of the quasi-flat zones to video data and a new filtering method. The segmentation is performed interactively by the user which has to draw markers on the objects of interest, in order to guide the merging of the quasi-flat zones which compose these objects. This process is performed on a region adjacency graph which contains spatiotemporal quasi-flat zones as nodes and their spatiotemporal adjacency relations as edges. The use of such structure provides a low computation cost. Obtained video objects are then used in an interactive mining process guided by descriptors automatically extracted from the video and information given by the user. The high interactivity with the user, both at the segmentation step and at the mining step promotes synergy between digital data and human interpretation.